The EvoSpace Model for Pool-Based Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Mario Garcia-Valdez and Leonardo Trujillo and 
                 Juan Julian {Merelo Guervos} and 
                 Francisco {Fernandez de Vega} and Gustavo Olague",
  title =        "The EvoSpace Model for Pool-Based Evolutionary
  journal =      "Journal of Grid Computing",
  year =         "2015",
  volume =       "13",
  number =       "3",
  pages =        "329--349",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Pool-based
                 evolutionary algorithms, Distributed evolutionary
                 algorithms, Heterogeneous computing platforms for
                 bioinspired algorithms, Parameter setting",
  ISSN =         "1572-9184",
  URL =          "",
  DOI =          "doi:10.1007/s10723-014-9319-2",
  abstract =     "This work presents the EvoSpace model for the
                 development of pool-based evolutionary algorithms
                 (Pool-EA). Conceptually, the EvoSpace model is built
                 around a central repository or population store,
                 incorporating some of the principles of the tuple-space
                 model and adding additional features to tackle some of
                 the issues associated with Pool-EAs; such as, work
                 redundancy, starvation of the population pool,
                 unreliability of connected clients or workers, and a
                 large parameter space. The model is intended as a
                 platform to develop search algorithms that take an
                 opportunistic approach to computing, allowing the
                 exploitation of freely available services over the
                 Internet or volunteer computing resources within a
                 local network. A comprehensive analysis of the model at
                 both the conceptual and implementation levels is
                 provided, evaluating performance based on efficiency,
                 optima found and speedup, while providing a comparison
                 with a standard EA and an island-based model. The
                 issues of lost connections and system parametrization
                 are studied and validated experimentally with
                 encouraging results, that suggest how EvoSpace can be
                 used to develop and implement different Pool-EAs for
                 search and optimization.",

Genetic Programming entries for Mario Garcia-Valdez Leonardo Trujillo Juan Julian Merelo Francisco Fernandez de Vega Gustavo Olague